It has always bothered me that I was told in my Calculus of Variations class that it's only possible to minimize a function with respect to one objective. Obviously sometimes it is possible to minimize with respect to more than one objective, depending on the function and the constraints.
I just started reading about Geometric Calculus yesterday, and woke up this morning thinking that geometric calculus should provide a good approach to dealing with those solvable multiobjective optimization problems, if only because of the peoperties of "antimultivectors". In particular, it seems that Geometric Calculus might be just right for describing reduced-dimension manifolds in a space of many variables.
Is there a good paper, or set of papers, that describe such an approach to optimization?